Refurbishment Grading & Routing

Retailers projected $890 billion in returns for 2024, representing 16.9% of annual sales, according to the National Retail Federation. Returns are particularly an issue for online retailers, as the ecommerce return rate was 24.5% in 2024 versus 8.7% for in-store purchases, according to credit card company Capital One.

This immense volume of returns creates operational and financial pressure. Manual grading of returned items to gauge their suitability for resale introduces subjectivity, as human operators may disagree or misclassify items. That increase costs, drives repeat returns and undermines trust in refurbished goods.

Recommerce—the buying and selling of returned, refurbished or pre-owned items—is expected to grow significantly, reaching $64.29 billion by 2025 and expanding at a compound annual growth rate of 9.4% through 2029, according to Statista. Yet many retailers struggle to capture this value. Inefficient grading prevents accurate routing of items into resale, refurbishment, or recycling channels, limiting recovery. Processing returns costs U.S. retailers an estimated $400 billion annually, with each return costing between 20% and 65% of an item’s original value, per McKinsey & Company. These economics highlight the urgency of automated, consistent grading at scale.

Computer vision and AI technologies offer a more reliable way to grade and route returns. Deep learning–based computer vision detects scratches, cracks, and other flaws, applying consistent grading criteria. Automated imaging captures multiple product views, ensuring detail, while AI models catalog cosmetic issues and assign condition grades such as A, B, or C.

Machine learning models improve continuously as they train on growing datasets of graded items, making them adept at spotting subtle condition variations. Integration with warehouse management systems can automate routing decisions, reducing dependence on manual labor.

Challenges remain. The technology requires standardized imaging setups, consistent lighting, and large training datasets. Functional testing of electronics often still needs supplementary checks. Variability in product types and non-standard packaging also add complexity. Despite these hurdles, AI-driven grading reduces subjectivity and accelerates decision-making.

Microland has integrated AI-driven grading into its returns and IT asset disposition operations, enabling efficient processing of large device volumes. Teams can more quickly determine whether items should be refurbished, resold, or recycled.

Optoro, a reverse logistics technology provider, reports processing more than 100 million returns using automated grading. In one case, an online marketplace for home remodeling used automated routing to resell more than 91% of its returns, boosting net recovery by 150% and cutting exception rates by more than half.

Urban Threads, a fashion retailer, deployed AI scanning to detect fabric flaws. This reduced processing time from 14 days to four days and lowered refund complaints by 30%.

In smartphones, machine vision has become central to profitable refurbishment. By detecting every cosmetic and functional issue, AI enables accurate grading, stronger resale values, and higher customer satisfaction. McKinsey research shows that remanufacturing industrial products delivers 40% to 65% more economic benefit compared to producing new ones, underscoring the fiscal impact of improved grading accuracy.

The market for AI-powered grading and routing spans computer vision specialists and full reverse logistics platforms. Key providers include:

• Optoro: Returns management platform with SmartDisposition engine and AI-powered grading.
• BackMarket: Global refurbishment marketplace with standardized grading for electronics.
• Microland: AI-driven grading systems for IT asset disposition and reverse logistics.
• OptoFidelity: Machine vision systems for smartphone refurbishment with AI defect detection.
• Recycleye: Computer vision technology for electronics recycling and material recovery.
• Loop Returns: Returns management software with grading features for fashion and apparel.
• Trove: Recommerce platform with automated quality assessment tools.
• B-Stock: Liquidation marketplace applying grading standards to bulk merchandise.
• Liquidity Services: Reverse logistics platform with automated grading for enterprise clients.
• ThredUp: Fashion resale company using AI for identification, grading, and pricing.

Organizations choosing providers should weigh integration needs, scalability, accuracy, and total cost of ownership. Vendors with domain expertise in a retailer’s product categories often deliver higher performance.

Future advances will link grading more tightly with circular commerce. Retailers are embedding recommerce into their supply chains, using automated routing for resale as part of inventory planning and sustainability goals. As grading systems merge with warehouse automation, workflows will streamline from return receipt to final disposition.

Automated grading is reshaping reverse logistics. By reducing subjectivity and accelerating decision-making, AI unlocks greater resale and refurbishment value while improving customer satisfaction. Technology is still maturing, but as it integrates more fully into supply chain and circular economy strategies, it will become indispensable for managing returns profitably and sustainably.